---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- f1
- accuracy
model-index:
- name: phrasebank-sentiment-analysis
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: financial_phrasebank
      type: financial_phrasebank
      config: sentences_50agree
      split: train
      args: sentences_50agree
    metrics:
    - name: F1
      type: f1
      value:
        f1: 0.8172545518133599
    - name: Accuracy
      type: accuracy
      value:
        accuracy: 0.8328748280605227
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# phrasebank-sentiment-analysis

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7236
- F1: {'f1': 0.8172545518133599}
- Accuracy: {'accuracy': 0.8328748280605227}

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | F1                         | Accuracy                         |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------:|:--------------------------------:|
| 0.3941        | 0.94  | 100  | 0.4098          | {'f1': 0.7962755487135231} | {'accuracy': 0.828060522696011}  |
| 0.1921        | 1.89  | 200  | 0.5360          | {'f1': 0.8094154455783058} | {'accuracy': 0.8321870701513068} |
| 0.0873        | 2.83  | 300  | 0.7086          | {'f1': 0.8146311198809535} | {'accuracy': 0.8301237964236589} |
| 0.0404        | 3.77  | 400  | 0.7236          | {'f1': 0.8172545518133599} | {'accuracy': 0.8328748280605227} |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3